Machine learning prediction of self-assembly and analysis of molecular structure dependence on the critical packing parameter

TL;DR

This study employs machine learning to predict self-assembly structures of amphiphilic molecules from their chemical structures, revealing key factors influencing assembly and aiding molecular design.

Contribution

It introduces a machine learning approach to directly predict self-assembled structures from chemical structures and analyzes the influence of molecular features on assembly behavior.

Findings

Random Forest and GRU models achieved high predictive accuracy

Amphiphilic nature significantly affects self-assembly structures

Proper molecular structure representation is crucial for model performance

Abstract

Amphiphilic molecules spontaneously form self-assembly structures based on physical conditions such as molecular structure, concentration, and temperature. These structures exhibit various useful functions according to their morphology. The concept of the critical packing parameter serves to correlate self-organized structures with chemical composition. However, unless both molecular arrangement and self-assembly patterns are understood, direct computational utilization for molecular design remains challenging. In this study, we attempt to predict the self-assembled structure of a molecule directly from its chemical structure and analyze factors influencing it using machine learning. Dissipative particle dynamics simulations were used to reproduce many self-assembly structures composed of various chemical structures, and their critical packing parameters were calculated. A machine learning model was built using the chemical structures as input data and the critical packing parameters as output data.As a result, both Random Forest and a type of Recurrent Neural Network known as GRU demonstrated high predictive accuracy. It has been revealed through feature importance analysis and dependence on sample size that the amphiphilic nature of molecules significantly influences the self-assembly structures. Additionally, the importance of selecting an appropriate molecular structure representation for each algorithm has been emphasized. The results of this research will help to further streamline product development in the fields of materials science, materials chemistry, and medical materials.